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Abstract: Many kernel learning algorithms, including support vector machines, result in a kernel machine, such as a kernel classifier, whose key component is a weight vector in a feature space implicitly introduced by a positive definite kernel function. This weight vector is usually obtained by solving a convex optimization problem. Based on this fact we present a direct method to build sparse kernel learning algorithms by adding one more constraint to the original convex optimization problem, such ... (Update)
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BibTeX entry: (Update)
@misc{ wu-journal,
author = "Algorithms Mingrui Wu",
title = "Journal of Machine Learning Research 7 (2006) 603--624 Submitted 10/05;
Published 4/06 A Direct Method for Building Sparse Kernel Learning",
url = "citeseer.ist.psu.edu/758511.html" }
Citations (may not include all citations):1662 Neural Networks for Pattern Recognition (context) - Bishop - 1995
1291 The Nature of Statistical Learning Theory (context) - Vapnik - 1995
119 Nonlinear Programming (context) - Mangasarian - 1969
113 Learning with Kernels (context) - Scholkopf, Smola - 2002
78 the limited memory BFGS method for large scale optimization - Liu, Nocedal - 1989
50 Sparse Bayesian learning and the relevance vector machine - Tipping - 2001
46 Simplified support vector decision rules - Burges - 1996
38 Choosing multiple parameters for support vector machines - Chapelle, Vapnik et al. - 2002
33 Least Squares Support Vector Machines (context) - Suykens, Gestel et al. - 2002
24 RSVM: reduced support vector machines (context) - Lee, Mangasarian - 2001
24 Combining support vector and mathematical programming method.. - Bennett - 1999
23 Kernel Methods for Pattern Analysis (context) - Shawe-Taylor, Cristianini - 2004
11 A robust minimax approach to classification - Lanckriet, Ghaoui et al. - 2002
4 Sparseness of support vector machine - Steinwart - 2003
4 A study on reduced support vector machines - Lin, Lin - 2003
3 Constructing descriptive and discriminative non-linear featu.. - Mika, Ratsch et al. - 2003
2 Some greedy learning algorithms for sparse regression and cl.. - Nair, Choudhury et al. - 2002
2 erential properties of the marginal function in mathematical.. (context) - Gauvin, Dubeau - 1982
1 Sparse gaussian processes using pseudo-inputs - Snelson, Ghahramani - 2006
1 Building sparse large margin classifiers (context) - Wu, Scholkopf et al. - 2005
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